AGENT SKILL
castai-deploy-integration
Deploy CAST AI across multi-cloud Kubernetes clusters with Terraform modules. Use when onboarding EKS, GKE, or AKS clusters to CAST AI using infrastructure-as-code patterns. Trigger with phrases like "deploy cast ai", "cast ai eks", "cast ai gke", "cast ai aks", "cast ai terraform module".
- License 许可
- MIT
- 最近更新
- 2026-06-24
- Stars
- ★ 67
- Forks
- 24
01 前言 Front Matter
- name
- castai-deploy-integration
- tags
- [ "saas", "kubernetes", "cost-optimization", "castai" ]
- author
- Jeremy Longshore <jeremy@intentsolutions.io>
- license
- MIT
- version
- 1.0.0
- description
- Deploy CAST AI across multi-cloud Kubernetes clusters with Terraform modules. Use when onboarding EKS, GKE, or AKS clusters to CAST AI using infrastructure-as-code patterns. Trigger with phrases like "deploy cast ai", "cast ai eks", "cast ai gke", "cast ai aks", "cast ai terraform module".
- allowed-tools
- Read, Write, Edit, Bash(terraform:*), Bash(helm:*), Bash(kubectl:*)
- compatible-with
- claude-code
03 SKILL.md 正文
CAST AI Deploy Integration
Overview
Deploy CAST AI to EKS, GKE, and AKS clusters using official Terraform modules. Each cloud provider has a dedicated CAST AI module that handles IAM roles, node configuration, and autoscaler setup.
Prerequisites
- Terraform 1.0+
- CAST AI Full Access API key
- Cloud provider credentials configured
- Existing Kubernetes cluster
Instructions
EKS Deployment
# main.tf -- EKS cluster onboarding
module "castai_eks" {
source = "castai/eks-cluster/castai"
version = "~> 3.0"
api_token = var.castai_api_token
aws_account_id = data.aws_caller_identity.current.account_id
aws_cluster_region = var.region
aws_cluster_name = var.cluster_name
# IAM role for CAST AI to manage nodes
aws_instance_profile_arn = aws_iam_instance_profile.castai.arn
# Autoscaler configuration
autoscaler_policies_json = jsonencode({
enabled = true
unschedulablePods = { enabled = true }
nodeDownscaler = {
enabled = true
emptyNodes = { enabled = true, delaySeconds = 300 }
}
spotInstances = {
enabled = true
spotDiversityEnabled = true
}
clusterLimits = {
enabled = true
cpu = { minCores = 4, maxCores = 200 }
}
})
# Node templates
default_node_configuration = module.castai_eks.castai_node_configurations["default"]
}
GKE Deployment
module "castai_gke" {
source = "castai/gke-cluster/castai"
version = "~> 2.0"
api_token = var.castai_api_token
project_id = var.gcp_project_id
gke_cluster_name = var.cluster_name
gke_cluster_location = var.region
gke_credentials = base64decode(
google_container_cluster.this.master_auth[0].cluster_ca_certificate
)
autoscaler_policies_json = jsonencode({
enabled = true
unschedulablePods = { enabled = true }
nodeDownscaler = {
enabled = true
emptyNodes = { enabled = true, delaySeconds = 300 }
}
})
}
AKS Deployment
module "castai_aks" {
source = "castai/aks/castai"
version = "~> 1.0"
api_token = var.castai_api_token
aks_cluster_name = var.cluster_name
aks_cluster_region = var.region
node_resource_group = azurerm_kubernetes_cluster.this.node_resource_group
azure_subscription_id = data.azurerm_subscription.current.subscription_id
azure_tenant_id = data.azurerm_client_config.current.tenant_id
autoscaler_policies_json = jsonencode({
enabled = true
unschedulablePods = { enabled = true }
spotInstances = { enabled = true }
})
}
Multi-Cluster Deployment Pattern
# Deploy CAST AI across all clusters with a for_each
variable "clusters" {
type = map(object({
name = string
provider = string # eks, gke, aks
region = string
max_cpu = number
}))
}
# Then reference the appropriate module per provider
Error Handling
| Issue | Cause | Solution |
|---|---|---|
| IAM role error | Missing permissions | Check CAST AI IAM docs for required policies |
| Module version conflict | Terraform lock | Run terraform init -upgrade |
| Cluster not appearing | Wrong credentials | Verify cloud provider auth |
| Policies not applying | JSON encoding error | Validate jsonencode() output |
Resources
Next Steps
For webhook-based automation, see castai-webhooks-events.